Open Access

Derivation and validation of a mortality risk prediction model using global longitudinal strain in patients with acute heart failure

logo

Date: 9 December 2019
Journal: European Heart Journal - Cardiovascular Imaging , Volume 21 , Issue 12 , Pages 1412 - 1420
Authors: I. Hwang , G. Cho , H. Choi , Y. Yoon , J. Park , J. Park , J. Park , S. Lee , H. Kim , Y. Kim , D. Sohn

ESC Journals

AbstractAims

To develop a mortality risk prediction model in patients with acute heart failure (AHF), using left ventricular (LV) function parameters with clinical factors.

Methods and results

In total, 4312 patients admitted for AHF were retrospectively identified from three tertiary centres, and echocardiographic parameters including LV ejection fraction (LV-EF) and LV global longitudinal strain (LV-GLS) were measured in a core laboratory. The full set of risk factors was available in 3248 patients. Using Cox proportional hazards model, we developed a mortality risk prediction model in 1859 patients from two centres (derivation cohort) and validated the model in 1389 patients from one centre (validation cohort). During 32 (interquartile range 13–54) months of follow-up, 1285 patients (39.6%) died. Significant predictors for mortality were age, diabetes, diastolic blood pressure, body mass index, natriuretic peptide, glomerular filtration rate, failure to prescribe beta-blockers, failure to prescribe renin–angiotensin system blockers, and LV-GLS; however, LV-EF was not a significant predictor. Final model including these predictors to estimate individual probabilities of mortality had C-statistics of 0.75 [95% confidence interval (CI) 0.73–0.78; P <0.001] in the derivation cohort and 0.78 (95% CI 0.75–0.80; P <0.001) in the validation cohort. The prediction model had good performance in both heart failure (HF) with reduced EF, HF with mid-range EF, and HF with preserved EF.

Conclusion

We developed a mortality risk prediction model for patients with AHF incorporating LV-GLS as the LV function parameter, and other clinical factors. Our model provides an accurate prediction of mortality and may provide reliable risk stratification in AHF patients.

About the contributors

In-Chang Hwang

Role: Author

Goo-Yeong Cho

Seongnam (Seoul National University Bundang Hospital)

Role: Author

Hong-Mi Choi

Role: Author